A Subject-Independent Brain-Computer Interface Framework Based on Supervised Autoencoder.

A calibration procedure is required in motor imagery-based brain-computer interface (MI-BCI) to tune the system for new users. This procedure is time-consuming and prevents naive users from using the system immediately. Developing a subject-independent MI-BCI system to reduce the calibration phase is still challenging due to the subject-dependent characteristics of the MI signals.

Unsupervised Motor Imagery Saliency Detection Based on Self-Attention Mechanism.

Detecting the salient parts of motor-imagery electroencephalogram (MI-EEG) signals can enhance the performance of the brain-computer interface (BCI) system and reduce the computational burden required for processing lengthy MI-EEG signals. In this paper, we propose an unsupervised method based on the self-attention mechanism to detect the salient intervals of MI-EEG signals automatically. Our suggested method can be used as a preprocessing step within any BCI algorithm to enhance its performance.

Command detection and classification in tongue drive assistive technology.

Tongue Drive System (TDS) is a new assistive technology that enables individuals with severe disabilities such as those with spinal cord injury (SCI) to regain environmental control using their tongue motion. We have developed a new sensor signal processing (SSP) algorithm which uses four 3-axial magneto-resistive sensor outputs to accurately detect and classify between seven different user-control commands in stationary as well as mobile conditions. The new algorithm employs a two-stage classification method with a combination of 9 classifiers to discriminate between 4 commands on the left or right side of the oral cavity (one neutral command shared on both sides).